The STEM Teacher Pipeline in Washington State: A Comprehensive Analysis of Preservice Predictors of STEM Teacher Career Paths and Effectiveness

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There is a shortage of STEM teachers in middle and high schools and a concern about improving the efficacy of STEM teachers. Despite a tremendous investment in interventions targeted to the existing STEM teacher workforce, our nation has made little progress in identifying specific interventions that can improve STEM teacher effectiveness or increase the probability that individuals trained to be STEM teachers actually enter the public teaching workforce. By focusing exclusively on preservice indicators of STEM teacher quality, many of which are policy manipulable (e.g., the curriculum required by TEPs or the assignment of prospective teachers to student teaching positions), this project seeks to provide meaningful evidence to teacher education programs and policymakers about preservice interventions that could improve the quality of the STEM teacher workforce. It will also provide evidence about the preservice experiences that predict which prospective STEM teachers ultimately enter the teaching workforce and the earnings of prospective STEM teachers who do not, and will inform efforts to target and recruit the next generation of STEM teachers. This research model can also be replicated in other states to help enhance our understanding of teacher education and workforce nationwide. This project is supported by the Education and Human Resources Core Research Program, which funds fundamental research in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. This project will be the first statewide analysis of the STEM teacher pipeline in Washington State that will leverage a unique longitudinal dataset. This dataset is unique in that it will connect detailed information about preservice teacher education experiences - such as transcript and student teaching data - to outcomes for all individuals trained to be teachers within specific teacher education programs in Washington State. The project will merge these data to longitudinal data from Washington State's new P-20 data warehouse and survey data of teacher education faculty and school district administrators to investigate three broad research questions: (1) What aspects of preservice teacher education are predictive of the probability and timing of public teaching workforce entry for prospective STEM teachers?; (2) Where do prospective STEM teachers who never enter the public teaching workforce end up employed, and what are their starting salaries in these positions relative to starting teaching salaries in the state's single salary schedule?; and (3) What preservice experiences and characteristics (including measures of content-area preparation, pedagogical knowledge, clinical experiences, and the alignment or "match" between a candidate's teacher education experiences, student teaching experiences, and early-career teaching experiences) are predictive of future STEM teacher effectiveness? The descriptive analyses use split-population models and value-added models to investigate the first and third research question. For the second research question, the analyses will focus on descriptive statistics that compare outcomes for those teacher candidates who do and do not enter teaching, attending to mediating characteristics such as salaries and pay scales.